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use crate::dimensions::allocator::Allocator;
use crate::dimensions::{DefaultAllocator, DimName, VectorN, U3};
// This determines when to take into consideration the magnitude of the state_delta and
// prevents dividing by too small of a number.
const REL_ERR_THRESH: f64 = 0.1;
/// The Error Control trait manages how a propagator computes the error in the current step.
pub trait ErrorCtrl
where
Self: Copy,
{
/// Computes the actual error of the current step.
///
/// The `error_est` is the estimated error computed from the difference in the two stages of
/// of the RK propagator. The `candidate` variable is the candidate state, and `cur_state` is
/// the current state. This function must return the error.
fn estimate<N: DimName>(
error_est: &VectorN<f64, N>,
candidate: &VectorN<f64, N>,
cur_state: &VectorN<f64, N>,
) -> f64
where
DefaultAllocator: Allocator<f64, N>;
}
/// A largest error control which effectively computes the largest error at each component
///
/// This is a standard error computation algorithm, but it's argubly bad if the state's components have different units.
/// It calculates the largest local estimate of the error from the integration (`error_est`)
/// given the difference in the candidate state and the previous state (`state_delta`).
/// This error estimator is from the physical model estimator of GMAT
/// (Source)[https://github.com/ChristopherRabotin/GMAT/blob/37201a6290e7f7b941bc98ee973a527a5857104b/src/base/forcemodel/PhysicalModel.cpp#L987]
#[derive(Clone, Copy)]
pub struct LargestError;
impl ErrorCtrl for LargestError {
fn estimate<N: DimName>(
error_est: &VectorN<f64, N>,
candidate: &VectorN<f64, N>,
cur_state: &VectorN<f64, N>,
) -> f64
where
DefaultAllocator: Allocator<f64, N>,
{
let state_delta = candidate - cur_state;
let mut max_err = 0.0;
for (i, prop_err_i) in error_est.iter().enumerate() {
let err = if state_delta[i] > REL_ERR_THRESH {
(prop_err_i / state_delta[i]).abs()
} else {
prop_err_i.abs()
};
if err > max_err {
max_err = err;
}
}
max_err
}
}
/// A largest step error control which effectively computes the L1 norm of the provided Vector of size 3
///
/// Note that this error controller should be preferrably be used only with slices of a state with the same units.
/// For example, one should probably use this for position independently of using it for the velocity.
/// (Source)[https://github.com/ChristopherRabotin/GMAT/blob/37201a6290e7f7b941bc98ee973a527a5857104b/src/base/forcemodel/ODEModel.cpp#L3033]
#[derive(Clone, Copy)]
pub struct LargestStep;
impl ErrorCtrl for LargestStep {
fn estimate<N: DimName>(
error_est: &VectorN<f64, N>,
candidate: &VectorN<f64, N>,
cur_state: &VectorN<f64, N>,
) -> f64
where
DefaultAllocator: Allocator<f64, N>,
{
let state_delta = candidate - cur_state;
let mut mag = 0.0f64;
let mut err = 0.0f64;
for i in 0..N::dim() {
mag += state_delta[i].abs();
err += error_est[i].abs();
}
if mag > REL_ERR_THRESH {
err / mag
} else {
err
}
}
}
/// A largest state error control
///
/// (Source)[https://github.com/ChristopherRabotin/GMAT/blob/37201a6290e7f7b941bc98ee973a527a5857104b/src/base/forcemodel/ODEModel.cpp#L3018]
#[derive(Clone, Copy)]
pub struct LargestState;
impl ErrorCtrl for LargestState {
fn estimate<N: DimName>(
error_est: &VectorN<f64, N>,
candidate: &VectorN<f64, N>,
cur_state: &VectorN<f64, N>,
) -> f64
where
DefaultAllocator: Allocator<f64, N>,
{
let sum_state = candidate + cur_state;
let mut mag = 0.0f64;
let mut err = 0.0f64;
for i in 0..N::dim() {
mag += 0.5 * sum_state[i].abs();
err += error_est[i].abs();
}
if mag > REL_ERR_THRESH {
err / mag
} else {
err
}
}
}
/// An RSS step error control which effectively computes the L2 norm of the provided Vector of size 3
///
/// Note that this error controller should be preferrably be used only with slices of a state with the same units.
/// For example, one should probably use this for position independently of using it for the velocity.
/// (Source)[https://github.com/ChristopherRabotin/GMAT/blob/37201a6290e7f7b941bc98ee973a527a5857104b/src/base/forcemodel/ODEModel.cpp#L3045]
#[derive(Clone, Copy)]
pub struct RSSStep;
impl ErrorCtrl for RSSStep {
fn estimate<N: DimName>(
error_est: &VectorN<f64, N>,
candidate: &VectorN<f64, N>,
cur_state: &VectorN<f64, N>,
) -> f64
where
DefaultAllocator: Allocator<f64, N>,
{
let mag = (candidate - cur_state).norm();
let err = error_est.norm();
if mag > REL_ERR_THRESH {
err / mag
} else {
err
}
}
}
/// An RSS state error control: when in doubt, use this error controller, especially for high accurracy.
///
/// Here is the warning from GMAT R2016a on this error controller:
/// > This is a more stringent error control method than [`rss_step`] that is often used as the default in other software such as STK.
/// > If you set [the] accuracy to a very small number, 1e-13 for example, and set the error control to [`rss_step`], integrator
/// > performance will be poor, for little if any improvement in the accuracy of the orbit integration.
/// For more best practices of these integrators (which clone those in GMAT), please refer to the
/// [GMAT reference](https://github.com/ChristopherRabotin/GMAT/blob/37201a6290e7f7b941bc98ee973a527a5857104b/doc/help/src/Resource_NumericalIntegrators.xml#L1292).
/// (Source)[https://github.com/ChristopherRabotin/GMAT/blob/37201a6290e7f7b941bc98ee973a527a5857104b/src/base/forcemodel/ODEModel.cpp#L3004]
#[derive(Clone, Copy)]
pub struct RSSState;
impl ErrorCtrl for RSSState {
fn estimate<N: DimName>(
error_est: &VectorN<f64, N>,
candidate: &VectorN<f64, N>,
cur_state: &VectorN<f64, N>,
) -> f64
where
DefaultAllocator: Allocator<f64, N>,
{
let mag = 0.5 * (candidate + cur_state).norm();
let err = error_est.norm();
if mag > REL_ERR_THRESH {
err / mag
} else {
err
}
}
}
/// An RSS state error control which effectively for the provided vector
/// composed of two vectors of the same unit, both of size 3 (e.g. position + velocity).
#[derive(Clone, Copy)]
pub struct RSSStatePV;
impl ErrorCtrl for RSSStatePV {
fn estimate<N: DimName>(
error_est: &VectorN<f64, N>,
candidate: &VectorN<f64, N>,
cur_state: &VectorN<f64, N>,
) -> f64
where
DefaultAllocator: Allocator<f64, N>,
{
let err_radius = RSSState::estimate::<U3>(
&error_est.fixed_rows::<U3>(0).into_owned(),
&candidate.fixed_rows::<U3>(0).into_owned(),
&cur_state.fixed_rows::<U3>(0).into_owned(),
);
let err_velocity = RSSState::estimate::<U3>(
&error_est.fixed_rows::<U3>(3).into_owned(),
&candidate.fixed_rows::<U3>(3).into_owned(),
&cur_state.fixed_rows::<U3>(3).into_owned(),
);
if err_radius > err_velocity {
err_radius
} else {
err_velocity
}
}
}
/// An RSS state error control which effectively for the provided vector
/// composed of two vectors of the same unit, both of size 3 (e.g. position + velocity).
#[derive(Clone, Copy)]
pub struct RSSStepPV;
impl ErrorCtrl for RSSStepPV {
fn estimate<N: DimName>(
error_est: &VectorN<f64, N>,
candidate: &VectorN<f64, N>,
cur_state: &VectorN<f64, N>,
) -> f64
where
DefaultAllocator: Allocator<f64, N>,
{
let err_radius = RSSStep::estimate::<U3>(
&error_est.fixed_rows::<U3>(0).into_owned(),
&candidate.fixed_rows::<U3>(0).into_owned(),
&cur_state.fixed_rows::<U3>(0).into_owned(),
);
let err_velocity = RSSStep::estimate::<U3>(
&error_est.fixed_rows::<U3>(3).into_owned(),
&candidate.fixed_rows::<U3>(3).into_owned(),
&cur_state.fixed_rows::<U3>(3).into_owned(),
);
if err_radius > err_velocity {
err_radius
} else {
err_velocity
}
}
}
/// An RSS state error control which effectively for the provided vector
/// composed of two vectors of the same unit, both of size 3 (e.g. position + velocity).
#[derive(Clone, Copy)]
pub struct RSSStepPVStm;
impl ErrorCtrl for RSSStepPVStm {
fn estimate<N: DimName>(
error_est: &VectorN<f64, N>,
candidate: &VectorN<f64, N>,
cur_state: &VectorN<f64, N>,
) -> f64
where
DefaultAllocator: Allocator<f64, N>,
{
let err_radius = RSSStep::estimate::<U3>(
&error_est.fixed_rows::<U3>(0).into_owned(),
&candidate.fixed_rows::<U3>(0).into_owned(),
&cur_state.fixed_rows::<U3>(0).into_owned(),
);
let err_velocity = RSSStep::estimate::<U3>(
&error_est.fixed_rows::<U3>(3).into_owned(),
&candidate.fixed_rows::<U3>(3).into_owned(),
&cur_state.fixed_rows::<U3>(3).into_owned(),
);
let err_cov_radius = RSSStep::estimate::<U3>(
&VectorN::<f64, U3>::new(error_est[6], error_est[6 + 7], error_est[6 + 14]),
&VectorN::<f64, U3>::new(candidate[6], candidate[6 + 7], candidate[6 + 14]),
&VectorN::<f64, U3>::new(cur_state[6], cur_state[6 + 7], cur_state[6 + 14]),
);
let err_cov_velocity = RSSStep::estimate::<U3>(
&VectorN::<f64, U3>::new(error_est[6 + 21], error_est[6 + 28], error_est[6 + 35]),
&VectorN::<f64, U3>::new(candidate[6 + 21], candidate[6 + 28], candidate[6 + 35]),
&VectorN::<f64, U3>::new(cur_state[6 + 21], cur_state[6 + 28], cur_state[6 + 35]),
);
let errs = vec![err_radius, err_velocity, err_cov_radius, err_cov_velocity];
let mut max_err = 0.0;
for err in errs {
if err > max_err {
max_err = err;
}
}
max_err
}
}